VIS assignment-3

knitr::opts_chunk$set(echo=TRUE)

library(sf)
library(tidyverse)
library(ggthemes)
library(ggspatial)
library(units)
library(nngeo)

Downloading Data

I decided to download the following data.

1. Boundaries of Community Districts (polygon) : com_district

2. Open Space (Parks) (polygon) : open_space

3. Locations of the Subway Stations (point) : sub_station

4. NYC Free Wifi Locations (point) : free_wifi

com_district <- st_read("https://data.cityofnewyork.us/api/geospatial/yfnk-k7r4?method=export&format=KML", quiet = TRUE)

open_space <- st_read("https://data.cityofnewyork.us/api/geospatial/g84h-jbjm?method=export&format=KML", quiet = TRUE)

sub_station <- st_read("https://data.cityofnewyork.us/api/geospatial/arq3-7z49?method=export&format=KML", quiet = TRUE)

free_wifi <- st_read("https://data.cityofnewyork.us/api/geospatial/a9we-mtpn?method=export&format=KML", quiet=TRUE)

Transfomring Data

NY_state_plane <- "+proj=lcc +lat_1=40.66666666666666 +lat_2=41.03333333333333 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"

com_district <-com_district %>%
  st_transform(NY_state_plane) 

open_space  <- open_space  %>%
  st_transform(NY_state_plane)

sub_station <- sub_station %>%
  st_transform(NY_state_plane)

free_wifi <- free_wifi %>%
  st_transform(NY_state_plane)

Creating a Basic Map

ggplot(com_district) +
  geom_sf(color = "lightgray") +
  geom_sf(data = open_space, fill = "darkgreen", color= NA) + 
  geom_sf(data = sub_station, color = "firebrick3", size = 0.1) +
  geom_sf(data = free_wifi, color = "royalblue3", size = 0.1) + 
  theme_map() + 
  annotation_scale() 

1. A number and proportion of subway stations (point) within a certian distance of free-wifi location (point)

Creating a buffer around free wifi-zone (40m)

wifi_buffer <- st_buffer(free_wifi, dist = 40) %>%
  st_union()

ggplot(wifi_buffer) +
  geom_sf() + 
  geom_sf(fill = "royalblue3", color = NA) +
  theme_map()

Subsetting points with a polygon

sub_station_wifi <- sub_station[wifi_buffer, ]
  
ggplot(sub_station_wifi) +
  geom_sf(data = wifi_buffer, fill = "royalblue3", color = NA) + 
  geom_sf(data = sub_station_wifi, color = "firebrick3", size = 0.1) +
  theme_map()

Joining two dataframes

sub_station <- sub_station %>%
  st_join(sub_station_wifi) %>%
  mutate(by_wifi = !is.na(Name.y))

Calculating the number of subway station within 40 meters of freewifi locations

n_sub_station_wifi <- sum(sub_station$by_wifi)

n_sub_station_wifi 

## [1] 281

Calculate the proportion of subway stations within 40 meters of freewifi locations

n_sub_station <- length(sub_station$by_wifi)
pct_sub_station_wifi <- n_sub_station_wifi/n_sub_station
pct_sub_station_wifi

## [1] 0.5940803

About 59% of all subway stations in NYC are within 40 meters of free-wifi locations.

Creating a Map

left_side <- st_bbox(sub_station)$xmin
top_side <- st_bbox(sub_station)$ymax

ggplot(com_district) +
  geom_sf(fill = "lightgray", color = "darkgray") +
  geom_sf(data = sub_station, size = 0.3,
          aes(color = by_wifi)) +
  scale_color_manual(values = c("firebrick3", "royalblue3"),
                    name = "NYC Subway Station \nby distance to a free-wifi",
                    labels = c("No Free-Wifi within 40m",
                               "Free-Wifi within 40m")) +
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "tr", 
                         style = north_arrow_minimal()) +
  annotate(geom = "text", x = left_side, y = top_side,
           label = paste("Of the ", 
                         prettyNum(n_sub_station, big.mark = ","),
                         " subway stations in NYC\n", 
                         prettyNum(n_sub_station_wifi, big.mark = ","),
                         " (", 
                         prettyNum(100*pct_sub_station_wifi, digits = 0),
                         "%) are within \n40 meters of free-wifi locations",
                         sep = ""), size = 3) +
  theme_map() +
  theme(panel.background = element_rect(fill = "aliceblue"),
        legend.background = element_rect(fill = alpha("white", 0.5), 
                                         color = "gray"))

2. Average Distance between the subway stations (point) and free-wifi location (point)

sub_station <- sub_station %>%
  mutate(sub_station_dist = st_nn(sub_station, free_wifi, 
                           returnDist = TRUE)$dist) %>%
  mutate(sub_station_dist = as.numeric(sub_station_dist))

## projected points

## 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%

avg_sub_dist <- mean(sub_station$sub_station_dist)
avg_sub_dist

## [1] 170.9093

right_side <- st_bbox(com_district)$xmax
left_side <- st_bbox(com_district)$xmin
top_side <- st_bbox(com_district)$ymax
bottom_side <- st_bbox(com_district)$ymin

ggplot(com_district) + 
  geom_sf(fill = "lightgray", color = "darkgray") + 
  geom_sf(data = sub_station, size = 0.3,
          aes(color = sub_station_dist)) + 
  coord_sf(xlim = c(left_side, right_side),
           ylim = c(bottom_side, top_side), expand = FALSE) + 
  scale_color_viridis_c(option = "plasma", 
                        name = "NYC Subway Stations\nby distance to Free-Wifi Location",
                        breaks = breaks <- seq(0, 1500, by = 500),
                        labels = paste(prettyNum(breaks, big.mark = ","), "meters from free-wifi")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  annotate(geom = "text", x = left_side + 500,
           y = top_side - 3500,
           label = paste("On average, a NYC Subway Station \n is ", 
                         prettyNum(avg_sub_dist, digits = 3),
                         " meters from a Free-Wifi Location.",
                         sep = ""),
           hjust = 0, vjust = 0, size =3) +

  theme_map() + 
  theme(panel.background = element_rect(fill = "aliceblue"),
        legend.background = element_rect(fill = alpha("white", 0.5), 
                                         color = "gray"))

3. The number and proportion of Free-Wifi locations (point) in each community district (polygon) in NYC

Number of Free-Wifi locations in each community district

com_district <- com_district %>%
  mutate(num_wifi = lengths(st_covers(com_district, free_wifi))) 

ggplot(com_district) + 
  geom_sf(color = NA, 
          aes(fill = num_wifi)) + 
  scale_fill_viridis_c(name = "NYC community districts\nby number of free-wifi locations",
                       breaks = breaks <- seq(0, 250, by = 50),
                       labels = paste(prettyNum(breaks), "Zones")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))

Proportions of Free-Wifi locations in each community district

I just want to additionally visualize the proportions of wifi locations in each community district to the whole wifi locations in NYC.

com_district <- com_district %>%
  mutate(num_wifi = lengths(st_covers(com_district, free_wifi))) %>%
  mutate(pct_wifi = num_wifi / nrow(free_wifi))

ggplot(com_district) + 
  geom_sf(color = NA, 
          aes(fill = pct_wifi)) + 
  scale_fill_viridis_c(name = "NYC community districts\nby the percentage of free-wifi zones",
                       breaks = breaks <- seq(0, 0.1, by = 0.02),
                       labels = paste(prettyNum(breaks*100), "%")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))

It is possible to know that in the place with the highest number of free-wifi zones - in this case, the midtown in Manhattan - more than 6% of free-wifi zones out of the whole zones are located in the Midtown.

4. Average Density of Free-Wifi Locations (point) within each Community Districts (polygon)

com_district <- com_district %>%
  mutate(area = set_units(st_area(com_district), km^2)) %>%
  mutate(wifi_dens = as.numeric(num_wifi / area)) 

ggplot(com_district) + 
  geom_sf(color = NA, 
          aes(fill = wifi_dens)) + 
  scale_fill_viridis_c(name = "NYC community districts\nby free-wifi density", 
                       breaks = breaks <- seq(0, 50, by = 10),
                       labels = paste(prettyNum(breaks), "wifi zones per square km")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))

5. The number and proportion of openspace (polygon) in each community districts (polygon) in NYC

Identifying the overlapping between community districts and openspaces

com_district <- com_district %>%
  mutate(num_open_space = lengths(st_overlaps(com_district, open_space))) %>%
  mutate(has_open_space = num_open_space > 0) 

n_open_space_district <- sum(com_district$has_open_space)

n_open_space_district

## [1] 65

Map on the presence of openspaces in each community districts

left_side  <- st_bbox(com_district)$xmin
top_side <- st_bbox(com_district)$ymax

ggplot(com_district) +
  geom_sf(fill = "lightgray", color = NA) +
  geom_sf(data = com_district,
          aes(fill = has_open_space)) +
  scale_fill_manual(values = c("cornsilk1", "darkgreen"),
          name = "Boston Neighborhoods\nby presence of an openspace", 
          labels = c("Districts without\nan overlapping openspace",
                     "Districts with an\noverlapping openspace")) +
  annotation_scale(location = "br") +
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) +
  annotate(geom = "text", x = left_side, 
           y = top_side - 1500, 
           label = paste(n_open_space_district ,
                         "of NYC's", 
                         length(com_district$Name),
                         "community districts contain\nor overlap with", 
                         "an openspace."),
           hjust = 0, vjust = 0, size = 3) +
  theme_map() +
  theme(panel.background = element_rect(fill = "aliceblue"),
        legend.background = element_rect(fill = alpha("white", 0.5), 
                                         color = "gray"))

Map on the number and proportion of openspaces in each community district

left_side <- st_bbox(open_space)$xmin
top_side <- st_bbox(open_space)$ymin 

ggplot(open_space) + 
  geom_sf(color = NA) + 
  geom_sf(data = com_district, 
          aes(fill = num_open_space)) + 
 scale_fill_viridis_c(option = "plasma", 
                      name = "NYC Community Districts \nby the Area of Openspace",
                      breaks = breaks <- seq(0, 50, by = 10),
                      labels = paste(prettyNum(breaks), "openspaces")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) +
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))

6. The number and proportion of subway stations (points) in each district (polygon) in NYC

com_district <- com_district %>%
  mutate(num_sub_station = lengths(st_covers(com_district, sub_station)))

ggplot(com_district) + 
  geom_sf(color = NA, 
          aes(fill = num_sub_station)) + 
  scale_fill_viridis_c(name = "NYC community districts\nby number of subway stations",
                       breaks = breaks <- seq(0, 50, by = 10),
                       labels = paste(prettyNum(breaks), "subway stations")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))

7. Average Density of subway stations (points) in each district (polygon) in NYC

com_district <- com_district %>%
  mutate(area = set_units(st_area(com_district), km^2)) %>%
  mutate(sub_station_dens = as.numeric(num_sub_station / area)) 

ggplot(com_district) + 
  geom_sf(color = NA, 
          aes(fill = sub_station_dens)) + 
  scale_fill_viridis_c(name = "NYC community districts\nby the density of subway stations",
                       breaks = breaks <- seq(0, 10, by = 2),
                       labels = paste(prettyNum(breaks), "subway stations per square km")) + 
  annotation_scale(location = "br") + 
  annotation_north_arrow(location = "tr",
                         style = north_arrow_minimal()) + 
  theme_map() + 
  theme(legend.background = element_rect(fill = alpha("white", 0.5),
                                         color = "gray"))